Choice of forest map has implications for policy analysis: A case study on the EU biofuel target

With the increasing availability of European and global forest maps, users are facing the difficult choice to select the most appropriate map for their purposes. Many of these maps are potential input datasets for forest-related applications for the European Union (EU), due to their spatial extent and harmonised approach at the European level. However, they possess different characteristics in terms of spatial detail or thematic accuracy. Little attention has been paid to the effect of these characteristics on simulation models and the resultant policy implications. In this study we tested whether the choice of a forest map has substantial influence on model output, i.e. if output differences can be related to the input differences. A sensitivity analysis of the spatially explicit Global Forest Model (G4M) was performed using four different forest maps: the pan-European high resolution forest/non-forest map (FMAP), the Corine Land Cover (CLC), the Calibrated European Forest Map (CEFM) and the Global Land Cover (GLC). Finally, the impact of potential differences owing to input datasets on decision-making was tested in a selected case study: reaching the EU 10% biofuel target through enhanced utilization of forest biomass. The sensitivity analysis showed that the choice of the forest cover map has a major influence on the model outputs in particular at the country-level, while having less influence at the EU27 level. Differences between the input datasets are strongly reflected in the outputs. Similarly, depending on the choice of the input alternate options for decision-making were found within the hypothesized biofuel target (case study), demonstrating a substantial value of information. In general, it was demonstrated that input maps are the major driver of decision-making if forest resource outputs of the model are their basis. Improvement of the input forest map would result in immediate benefit for a better decision-making basis. ⺠Sensitivity analysis of a simulation model showed strong influence of forest maps. ⺠Strongest effect on model outputs at country-level, less influence at EU27 level. ⺠An EU biofuel case study found similar effect of forest maps on decision-making. ⺠Importance of careful choice of forest input maps for models is highlighted. ⺠Map improvement brings immediate benefit to decision-making policy process.